Title: Teaching Computational Reproducibility for Neuroimaging

Journal Article · · Frontiers in Neuroscience (Online)
 [1];  [2];  [3];  [4]
  1. Univ. of California, Berkeley, CA (United States). Division of Biostatistics; Univ. of California, Berkeley, CA (United States). Berkeley Inst. for Data Science; DOE/OSTI
  2. Univ. of Birmingham (United Kingdom). College of Life and Environmental Sciences
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Applied Nuclear Physics Program
  4. McGill Univ., Montreal, QC (Canada). Neurology and Neurosurgery
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We describe a project-based introduction to reproducible and collaborative neuroimaging analysis. Traditional teaching on neuroimaging usually consists of a series of lectures that emphasize the big picture rather than the foundations on which the techniques are based. The lectures are often paired with practical workshops in which students run imaging analyses using the graphical interface of specific neuroimaging software packages. Our experience suggests that this combination leaves the student with a superficial understanding of the underlying ideas, and an informal, inefficient, and inaccurate approach to analysis. To address these problems, we based our course around a substantial open-ended group project. This allowed us to teach: (a) computational tools to ensure computationally reproducible work, such as the Unix command line, structured code, version control, automated testing, and code review and (b) a clear understanding of the statistical techniques used for a basic analysis of a single run in an MR scanner. The emphasis we put on the group project showed the importance of standard computational tools for accuracy, efficiency, and collaboration. The projects were broadly successful in engaging students in working reproducibly on real scientific questions. We propose that a course on this model should be the foundation for future programs in neuroimaging. We believe it will also serve as a model for teaching efficient and reproducible research in other fields of computational science.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Nuclear Physics (NP)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1628205
Journal Information:
Frontiers in Neuroscience (Online), Journal Name: Frontiers in Neuroscience (Online) Vol. 12; ISSN 1662-453X
Publisher:
Frontiers Research FoundationCopyright Statement
Country of Publication:
United States
Language:
English

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How to Use Replication Assignments for Teaching Integrity in Empirical Archaeology journal October 2019
How to use replication assignments for teaching integrity in empirical archaeology text January 2022

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Related Subjects

60 APPLIED LIFE SCIENCES
97 MATHEMATICS AND COMPUTING
Neurosciences & Neurology